Development and Testing of An Automatic Lung IMRT Planning Algorithm

Knowledge-based radiation treatment is an emerging concept in radiotherapy. It

mainly refers to the technique that can guide or automate treatment planning in

clinic by learning from prior knowledge. Dierent models are developed to realize

it, one of which is proposed by Yuan et al. at Duke for lung IMRT planning. This

model can automatically determine both beam conguration and optimization ob-

jectives with non-coplanar beams based on patient-specic anatomical information.

Although plans automatically generated by this model demonstrate equivalent or

better dosimetric quality compared to clinical approved plans, its validity and gener-

ality are limited due to the empirical assignment to a coecient called angle spread

constraint dened in the beam eciency index used for beam ranking. To eliminate

these limitations, a systematic study on this coecient is needed to acquire evidences

for its optimal value.

To achieve this purpose, eleven lung cancer patients with complex tumor shape

with non-coplanar beams adopted in clinical approved plans were retrospectively

studied in the frame of the automatic lung IMRT treatment algorithm. The primary

and boost plans used in three patients were treated as dierent cases due to the

dierent target size and shape. A total of 14 lung cases, thus, were re-planned using

the knowledge-based automatic lung IMRT planning algorithm by varying angle

spread constraint from 0 to 1 with increment of 0.2. A modied beam angle eciency

index used for navigate the beam selection was adopted. Great eorts were made to assure the quality of plans associated to every angle spread constraint as good

as possible. Important dosimetric parameters for PTV and OARs, quantitatively

re

ecting the plan quality, were extracted from the DVHs and analyzed as a function

of angle spread constraint for each case. Comparisons of these parameters between

clinical plans and model-based plans were evaluated by two-sampled Students t-tests,

and regression analysis on a composite index built on the percentage errors between

dosimetric parameters in the model-based plans and those in the clinical plans as a

function of angle spread constraint was performed.

Results show that model-based plans generally have equivalent or better quality

than clinical approved plans, qualitatively and quantitatively. All dosimetric param-

eters except those for lungs in the automatically generated plans are statistically

better or comparable to those in the clinical plans. On average, more than 15% re-

duction on conformity index and homogeneity index for PTV and V40, V60 for heart

while an 8% and 3% increase on V5, V20 for lungs, respectively, are observed. The

intra-plan comparison among model-based plans demonstrates that plan quality does

not change much with angle spread constraint larger than 0.4. Further examination

on the variation curve of the composite index as a function of angle spread constraint

shows that 0.6 is the optimal value that can result in statistically the best achievable

plans.

The association of HIV counseling and testing with HIV risk behaviors in a random population-based survey in Kisumu, Kenya.

HIV testing has been promoted as a key HIV prevention strategy in low-resource settings, despite studies showing variable impact on risk behavior. We sought to examine rates of HIV testing and the association between testing and sexual risk behaviors in Kisumu, Kenya. Participants were interviewed about HIV testing and sexual risk behaviors. They then underwent HIV serologic testing. We found that 47% of women and 36% of men reported prior testing. Two-thirds of participants who tested HIV-positive in this study reported no prior HIV test. Women who had undergone recent testing were less likely to report high-risk behaviors than women who had never been tested; this was not seen among men. Although rates of HIV testing were higher than seen in previous studies, the majority of HIV-infected people were unaware of their status. Efforts should be made to increase HIV testing among this population.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.